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Author: Tim Bernard Bigdeli
Publisher:
ISBN:
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Languages : en
Pages :
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Book Description
Etiological models of complex disease are elusive[46, 33, 9], as are consistently replicable findings for major genetic susceptibility loci[54, 14, 15, 24]. Commonly-cited explanations invoke low-frequency genomic variation[41], allelic heterogeneity at susceptibility loci[33, 30], variable etiological trajectories[18, 17], and epistatic effects between multiple loci; these represent among the most methodologically-challenging issues in molecular genetic studies of complex traits. The response has been con- sistently reactionary -- hypotheses regarding the relative contributions of known functional elements, or emphasizing a greater role of rare variation[46, 33] have undergone periodic revision, driving increasingly collaborative efforts to ascertain greater numbers of participants and which assay a rapidly-expanding catalogue of human genetic variation. Major deep-sequencing initiatives, such as the 1,000 Genomes Project, are currently identifying human polymorphic sites at frequencies previously unassailable and, not ten years after publication of the first major genome-wide association findings, re-sequencing has already begun to displace GWAS as the standard for genetic analysis of complex traits. With studies of complex disease primed for an unprecedented survey of human genetic variation, it is essential that human geneticists address several prominent, problematic aspects of this research. Realizations regarding the boundaries of human traits previously considered to be effectively disparate in presentation[44, 39, 35, 27, 25, 12, 4, 13], as well as profound insight into the extent of human genetic diversity[23, 22] are not without consequence. Whereas the resolution of fine-mapping studies have undergone persistent refinement, recent polygenic findings suggest a less discriminant basis of genetic liability, raising the question of what a given, unitary association finding actually represents. Furthermore, realistic expectations regarding the pattern of findings for a particular genetic factor between or even within populations remain unclear. Of interest herein are methodologies which exploit the finite extent of genomic variability within human populations to distinguish single-point and cumulative group differences in liability to complex traits, the range of allele frequencies for which common association tests are appropriate, and the relevant dimensionality of common genetic variation within ethnically-concordant but differentially ascertained populations. Using high-density SNP genotype data, we consider both hypothesis-driven and agnostic (genome-wide) approaches to association analysis, and address specific issues pertaining to empirical significance and the statistical properties of commonly- applied tests. Lastly, we demonstrate a novel perspective of genome-wide genetic "background" through exhaustive evaluation of fundamental, stochastic genetic processes in a sample of matched affected and unaffected siblings selected from high- density schizophrenia families.
Author: Tim Bernard Bigdeli
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Etiological models of complex disease are elusive[46, 33, 9], as are consistently replicable findings for major genetic susceptibility loci[54, 14, 15, 24]. Commonly-cited explanations invoke low-frequency genomic variation[41], allelic heterogeneity at susceptibility loci[33, 30], variable etiological trajectories[18, 17], and epistatic effects between multiple loci; these represent among the most methodologically-challenging issues in molecular genetic studies of complex traits. The response has been con- sistently reactionary -- hypotheses regarding the relative contributions of known functional elements, or emphasizing a greater role of rare variation[46, 33] have undergone periodic revision, driving increasingly collaborative efforts to ascertain greater numbers of participants and which assay a rapidly-expanding catalogue of human genetic variation. Major deep-sequencing initiatives, such as the 1,000 Genomes Project, are currently identifying human polymorphic sites at frequencies previously unassailable and, not ten years after publication of the first major genome-wide association findings, re-sequencing has already begun to displace GWAS as the standard for genetic analysis of complex traits. With studies of complex disease primed for an unprecedented survey of human genetic variation, it is essential that human geneticists address several prominent, problematic aspects of this research. Realizations regarding the boundaries of human traits previously considered to be effectively disparate in presentation[44, 39, 35, 27, 25, 12, 4, 13], as well as profound insight into the extent of human genetic diversity[23, 22] are not without consequence. Whereas the resolution of fine-mapping studies have undergone persistent refinement, recent polygenic findings suggest a less discriminant basis of genetic liability, raising the question of what a given, unitary association finding actually represents. Furthermore, realistic expectations regarding the pattern of findings for a particular genetic factor between or even within populations remain unclear. Of interest herein are methodologies which exploit the finite extent of genomic variability within human populations to distinguish single-point and cumulative group differences in liability to complex traits, the range of allele frequencies for which common association tests are appropriate, and the relevant dimensionality of common genetic variation within ethnically-concordant but differentially ascertained populations. Using high-density SNP genotype data, we consider both hypothesis-driven and agnostic (genome-wide) approaches to association analysis, and address specific issues pertaining to empirical significance and the statistical properties of commonly- applied tests. Lastly, we demonstrate a novel perspective of genome-wide genetic "background" through exhaustive evaluation of fundamental, stochastic genetic processes in a sample of matched affected and unaffected siblings selected from high- density schizophrenia families.
Author: D.C. Rao
Publisher: Academic Press
ISBN: 0080569110
Category : Medical
Languages : en
Pages : 788
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Book Description
The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Five sections on the latest advances in complex traits Methods for testing with ethical, legal, and social implications Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more
Author: Andrew H. Paterson
Publisher: CRC Press
ISBN: 1420049380
Category : Science
Languages : en
Pages : 320
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Book Description
In the past 10 years, contemporary geneticists using new molecular tools have been able to resolve complex traits into individual genetic components and describe each such component in detail. Molecular Dissection of Complex Traits summarizes the state of the art in molecular analysis of complex traits (QTL mapping), placing new developments in thi
Author: Christine Langhoff
Publisher: GRIN Verlag
ISBN: 3638195252
Category : Science
Languages : en
Pages : 12
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Book Description
Essay from the year 2002 in the subject Biology - Genetics / Gene Technology, grade: 2.1 (B), Oxford University (New College), language: English, abstract: Ultimately, the goal of genetics is the analysis of the genotype of organisms. But the genotype can be identified – and therefore studied – only through its phenotypic effect. This means that two genotypes are recognised as different from each other because the phenotypes of their carriers are different. A problem can be seen with this approach as the actual variation between organisms is usually quantitative, not qualitative. Many different genotypes may have the same average phenotype. At the same time, because of environmental variation, two individuals of the same genotype may not have the same phenotype. This lack of a one-to-one correspondence between genotype and phenotype obscures underlying Mendelian genetics. I am going to explore the use of various statistical techniques for studying quantitative traits with application to behavioural traits. I am also going to examine whether there are behavioural traits with sufficiently high heritabilities to give hope for gene searches and I am going to discuss the difficulties that confront molecular geneticists regarding psychiatric genetics.
Author: Yuan-Ming Zhang
Publisher: Frontiers Media SA
ISBN: 2832543693
Category : Science
Languages : en
Pages : 278
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Book Description
Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. However, there are still limits in current GWAS statistics. For example, (1) almost all the existing methods do not estimate additive and dominance effects in quantitative trait nucleotide (QTN) detection; (2) the methods for detecting QTN-by-environment interaction (QEI) are not straightforward and do not estimate additive and dominance effects as well as additive-by-environment and dominance-by-environment interaction effects, leading to unreliable results; and (3) no or too simple polygenic background controls have been employed in QTN-by-QTN interaction (QQI) detection. As a result, few studies of QEI and QQI for complex traits have been reported based on multiple-environment experiments. Recently, new statistical tools, including 3VmrMLM, have been developed to address these needs in GWAS. In 3VmrMLM, all the trait-associated effects, including QTN, QEI and QQI related effects, are compressed into a single effect-related vector, while all the polygenic backgrounds are compressed into a single polygenic effect matrix. These compressed parameters can be accurately and efficiently estimated through a unified mixed model analysis. To further validate these new GWAS methods, particularly 3VmrMLM, they should be rigorously tested in real data of various plants and a wide range of other species.
Author: Nicholas A. Furlotte
Publisher:
ISBN:
Category :
Languages : en
Pages : 105
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Book Description
Over the past two decades, major technological innovations have transformed the field of genetics allowing researchers to examine the relationship between genetic and phenotypic variation at an unprecedented level of granularity. As a result, genetics has increasingly become a data-driven science, demanding effective statistical procedures and efficient computational methods and necessitating a new interface that some refer to as computational genetics. In this dissertation, I focus on a few problems existing within this interface. First, I introduce a method for calculating gene coexpression in a way that is robust to statistical confounding introduced through expression hetero- geneity. Heterogeneity in experimental conditions causes separate microarrays to be more correlated than expected by chance. This additional correlation between arrays induces correlation between gene expression measurements, in effect causing spuri- ous gene coexpression. By formulating the problem of calculating coexpression in a linear mixed-model framework, I show how it is possible to account for the cor- relation between microarrays and produce coexpression values that are robust to ex- pression heterogeneity. Second, I introduce a meta-analysis technique that allows for genome-wide association studies to be combined across populations that are known to contain population structure. This development was motivated by a specific problem in mouse genetics, the aim of which is to utilize multiple mouse association studies jointly. I show that by combining the studies using meta-analysis, while accounting for population structure, the proposed method achieves increased statistical power and increased association resolution. Next, I will introduce a computational and statistical procedure for performing genome-wide association using longitudinal measurements. I show that by accounting for the genetic and environmental correlation between mea- surements originating from the same individual, it is possible to increase association power. Finally, I will introduce a statistical and computational construct called the matrix-variate linear mixed-model (mvLMM), which is used for multiple phenotype genome-wide association. I show how the application of this method results in increased association power over single trait mapping and leads to a dramatic reduction in computational time over classical multiple phenotype optimization procedures. For example, where a classically-based approach takes hours to perform parameter optimization for moderate sample sizes mvLMM takes minutes. This technique is both a generalization and improvement on the previously proposed longitudinal analysis technique and its innovation has the potential to impact many current problems in the field of computational genetics.
Author: Scott A. Rifkin
Publisher: Humana Press
ISBN: 9781617797866
Category : Medical
Languages : en
Pages : 331
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Book Description
Over the last two decades advances in genotyping technology, and the development of quantitative genetic analytical techniques have made it possible to dissect complex traits and link quantitative variation in traits to allelic variation on chromosomes or quantitative trait loci (QTLs). In Quantitative Trait Loci (QTLs):Methods and Protocols, expert researchers in the field detail methods and techniques that focus on specific components of the entire process of quantitative train loci experiments. These include methods and techniques for the mapping populations, identifying quantitative trait loci, extending the power of quantitative trait locus analysis, and case studies. Written in the highly successful Methods in Molecular BiologyTM series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Quantitative Trait Loci (QTLs):Methods and Protocols aids scientists in the further study of the links between phenotypic and genotypic variation in fields from medicine to agriculture, from molecular biology to evolution to ecology.
Author: Nicola J. Camp
Publisher: Springer Science & Business Media
ISBN: 1592591760
Category : Medical
Languages : en
Pages : 362
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Book Description
In Quantitative Trait Loci: Methods and Protocols, a panel of highly experienced statistical geneticists demonstrate in a step-by-step fashion how to successfully analyze quantitative trait data using a variety of methods and software for the detection and fine mapping of quantitative trait loci (QTL). Writing for the nonmathematician, these experts guide the investigator from the design stage of a project onwards, providing detailed explanations of how best to proceed with each specific analysis, to find and use appropriate software, and to interpret results. Worked examples, citations to key papers, and variations in method ease the way to understanding and successful studies. Among the cutting-edge techniques presented are QTDT methods, variance components methods, and the Markov Chain Monte Carlo method for joint linkage and segregation analysis.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309108675
Category : Social Science
Languages : en
Pages : 429
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Book Description
Biosocial Surveys analyzes the latest research on the increasing number of multipurpose household surveys that collect biological data along with the more familiar interviewerâ€"respondent information. This book serves as a follow-up to the 2003 volume, Cells and Surveys: Should Biological Measures Be Included in Social Science Research? and asks these questions: What have the social sciences, especially demography, learned from those efforts and the greater interdisciplinary communication that has resulted from them? Which biological or genetic information has proven most useful to researchers? How can better models be developed to help integrate biological and social science information in ways that can broaden scientific understanding? This volume contains a collection of 17 papers by distinguished experts in demography, biology, economics, epidemiology, and survey methodology. It is an invaluable sourcebook for social and behavioral science researchers who are working with biosocial data.
Author: Kathryn Burch
Publisher:
ISBN:
Category :
Languages : en
Pages : 128
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Book Description
A major finding from the last decade of genome-wide association studies (GWAS) is that variant-phenotype associations are significantly enriched in noncoding regulatory regions of the genome. This result suggests that GWAS associations localize variants that modulate phenotype via gene regulation as opposed to alterations in protein structure/function. However, for most complex traits, most aspects of genetic architecture-the number of causal variants/genes for a trait and the degree to which causal effect sizes are coupled with genomic features such as minor allele frequency (MAF) and linkage disequilibrium (LD)-remain actively debated. In this dissertation, I introduce three new methods to explore and quantitatively characterize complex-trait genetic architecture. First, I derive an unbiased estimator of genome-wide SNP-heritability under a very general random effects model that makes minimal assumptions on the underlying (unknown) genetic architecture of the trait. Second, I introduce a method for estimating the number of causal variants that are shared between two ancestral populations for a given trait, and I discuss the implications of the method and real-data results for improving polygenic risk prediction in ethnic minority populations. Third, I propose methods for partitioning the heritability of individual genes by MAF to identify disease-relevant genes, with the hypothesis that some disease-relevant genes may have relatively large heritability contributions from rare and low-frequency variants while still having low total gene-level heritability.
